Breast cancer This analysis was conducted by two professional biologists. is considered a leading cause of morbidity and mortality among women worldwide. Above 12% of the women in the United States are diagnosed with breast cancer during their lifetime [1]}. Alteration of gene regulation has been widely studied in this context [2]}, with a special focus on dynamic changes in gene co-expression modules. Under the Cancer Genome Atlas (TCGA) program, a community-scale effort has been directed towards multi-omic molecular profiling of breast tumors in hundreds of patients [3]}. We use Fragments Per Kilobase of transcript per Million mapped reads (FPKM) normalized gene expression data from TCGA to understand if disguising community affiliation plays a role in the pathogenesis of critical diseases such as cancers. To achieve this, we construct a control and a cancer-specific co-expression network based on transcriptomic profiles of 1,097 normal (as controls) and 113 tumor samples obtained from the TCGA repository. Both networks spanned the same set of 1,000 genes (1,000 nodes). Two nodes are connected by an edge when the Pearson's correlation coefficient computed across the entire spectrum of control/tumor samples qualifies a cut-off value of \(0.6\)  (\(12,161\)  edges). Deleterious mutations in cancer cause wide-spread loss-of-function events, which are often manifested by changes in gene expression levels.
